mlsanityData to 14 Oct 2026

Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles

arXiv 2609.19831 ↗First posted 17 Sep 2026cs.IR cs.AIcited by 0

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Abstract (arXiv)

In this reproducibility study, we investigate the transparency and scrutability of recommender systems enhanced by incorporating generated natural-language user profiles that represent user preferences. The original paper explores the synthesis of user profiles from raw user-generated review text across domains such as movies and accommodations (Amazon Movies & TV, TripAdvisor). Crucially, these natural-language user profiles enable direct user interaction and intervention, allowing users to customize recommendations by correcting misattributed preferences or addressing cold-start settings. We successfully reproduce the core findings of the original study. Additionally, we extend the evaluation by conducting systematic context ablation experiments, multi-seed stability across five distinct random seeds to establish statistical reliability, and a mechanistic interpretability analysis using the nnsight framework to probe internal model representations under counterfactual profile perturbations. Our findings verify the original paper's claim that User Profile Recommendation (UPR) achieves competitive performance under its test-set reranking protocol and makes recommendations more transparent. Perturbing the natural-language profiles does change predictions, but it shifts predicted ratings uniformly across genres with no detectable genre-selective effect, leaving rankings unchanged even under direct activation steering. We trace this back to the rating-regression objective rather than the profile interface, with ranking-objective models clearly exceeding in this task.

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References · 5

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 520
  3. 2021Personalized Transformer for Explainable Recommendationcited by 59
  4. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  5. 2024Transparent and Scrutable Recommendations Using Natural Language User Profilescited by 24

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  5. 2025Can Large Language Models Understand Preferences in Personalized Recommendation?
  6. 2025ProEx: A Unified Framework Leveraging Large Language Model with Profile Extrapolation for Recommendation
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  8. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review